Multi-Temporal Surface Water Classification for Four Major Rivers from the Peruvian Amazon

DOI HANDLE Web Site Research Data 3 Citations 20 References Open Access
  • Margaret Kalacska
    Applied Remote Sensing Lab., Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada
  • J. Pablo Arroyo-Mora
    Flight Research Lab., National Research Council Canada, Ottawa, ON K1A 0R6, Canada
  • Oliver T. Coomes
    Applied Remote Sensing Lab., Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada
  • Yoshito Takasaki
    Graduate School of Economics, University of Tokyo, Tokyo 113-0033, Japan
  • Christian Abizaid
    Department of Geography & Planning, School of the Environment, University of Toronto, Toronto, ON M5S 3B3, Canada

Description

<jats:p>We describe a new minimum extent, persistent surface water classification for reaches of four major rivers in the Peruvian Amazon (i.e., Amazon, Napo, Pastaza, Ucayali). These data were generated by the Peruvian Amazon Rural Livelihoods and Poverty (PARLAP) Project which aims to better understand the nexus between livelihoods (e.g., fishing, agriculture, forest use, trade), poverty, and conservation in the Peruvian Amazon over a 35,000 km river network. Previous surface water datasets do not adequately capture the temporal changes in the course of the rivers, nor discriminate between primary main channel and non-main channel (e.g., oxbow lakes) water. We generated the surface water classifications in Google Earth Engine from Landsat TM 5, 7 ETM+, and 8 OLI satellite imagery for time periods from circa 1989, 2000, and 2015 using a hierarchical logical binary classification predominantly based on a modified Normalized Difference Water Index (mNDWI) and shortwave infrared surface reflectance. We included surface reflectance in the blue band and brightness temperature to minimize misclassification. High accuracies were achieved for all time periods (>90%).</jats:p>

Journal

  • Data

    Data 7 (1), 6-, 2022-01-06

    MDPI AG

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